The convergence of wireless networks and multimedia communications has resulted in rapid development of various services and increased competition between service providers. This, in turn, has caused a rise in user expectations with respect to wireless network service session performance. In particular, a user would like an increased Quality of Experience (QoE) of user's application session. Thus, user application specific network service session performance improvement has been one of the primary targets for the network service providers or operators. Such improvement typically require gathering of network service session level performance information so as to monitor service performance, assess performance degradation, and recommend corrective action for user specific application sessions.
However, existing techniques are limited in monitoring service performance for user specific application sessions. For example, existing techniques are plagued by data flooding and duplication issues and accuracy in key performance indicators (KPI's) generation issues. Data flooding and duplication may lead to delay in identification of relevant packets from collected packets. This may also result in information loss due to buffer-overflow. Additionally, it is not possible to get accurate KPI values without knowing exact number of instances of radio access network (RAN) protocol modules that is running in base station (BS). This is likely to lead to inaccurate KPI generation, which is not representing the appropriate application session level and network service-session level performance. Further, for example, existing techniques do not cater to different monitoring need for application-sessions. In other words, existing techniques are agnostic of user and application session, and monitor performance and collect performance data of application-sessions irrespective of their priorities, user attention level, and current performance level. This may result in ineffective monitoring of poor performing critical application sessions which may need immediate attention at any given moment.
Further, existing techniques are limited in assessing performance degradation for user specific application sessions. For example, existing techniques do not provide determination of Quality of Experience (QoE) for user application-session. Additionally, for example, existing techniques do not consider user feedback or user experience in determination of user application session performance, and, therefore, may not accurately represent actual user experience degradation. Further, for example, existing techniques that do provide for determination of subjective QoE (SQoE) as perceived by the end user may either perform incorrect determination of SQoE (in case of non-availability of application session performance information) or delayed determination of SQoE (in case of delayed arrival of application session performance information) due to air interface congestion. Such incorrect determination of SQoE or delayed determination of SQoE may result in ineffective assessment of application session performance.
Moreover, existing techniques are limited in recommending corrective action for user specific application sessions. For example, existing techniques do not provide identification of the neighboring cell (target cell) to move relevant users. Additionally, for example, existing techniques that provide for load based handover (LBHO) may not guarantee maintenance of user application session performance. The LBHO to a single random target BS may not be a panacea for all application session performance improvement or for all user applications. The LBHO to the single random target BS may improve some user application session, degrade some user application session, and discontinue some user application sessions. The random selection of one or more user for movement to the target BS with all its application-sessions may not be suitable corrective action for user specific application session performance improvement. On the contrary, such LBHO may degrade SQoE of application sessions with acceptable performance.